[1]} is proposed to solve the data-hungry problem to make vision transformers perform well on small datasets. Specifically, CCT gets the embeddings with convolutional layers and makes the input shape flexible by removing the positional embeddings. On top of that, CCT incorporates the Sequence Pooling at the end of the transformer layers to compute the weights of the output sequence.
